import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
from joblib import load


class NN(object):
    def __init__(self):
        self.loaded_model =tf.keras.models.load_model('timing/my_model.keras')
        self.scaler =load('timing/scaler_joblib')
    
    def get_hourly_trend(self):
        """
        """
        n_steps =7
        df_pivot = pd.read_csv('./timing/scenic_data.csv')
        # latest_data = df_pivot.iloc[-n_steps:].values  # 取后七天数据
        x_values = df_pivot.iloc[-n_steps:]
        x_values.iloc[-1, x_values.columns.get_loc('count')] = 0
        x_lasterst = x_values.values
        latest_data = x_lasterst.reshape(1, n_steps, x_lasterst.shape[1])

        # 预测与反归一化
        predicted = self.loaded_model.predict(latest_data)
        predicted_counts = self.scaler.inverse_transform(predicted)  # 反归一化
        predicted_counts[predicted_counts < 0] = 0  # 修正负值

        hourly_trend = predicted_counts[0].astype(int).tolist()
        print(hourly_trend)

if __name__ =='__main__':
    nn =NN()
    nn.get_hourly_trend()

